What is data science?

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For most organizations, data science is employed to transform data into value that might come in the form improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like.

"The amount of data you can grab, if you want, is immense, but if you're not doing anything with it, turning it into something interesting, what good is it? Data science is about giving that data a purpose," says Adam Hunt, chief data scientist at RiskIQ.

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Digital TransformationFinancial Services IndustryIT LeadershipIDG InsiderHow virtual reality is helping Embry-Riddle transform air crash educationTue, 12 Jun 2018 03:00:00 -0700Thor OlavsrudThor OlavsrudIn the past several years, commercially available headsets such as Facebook's Oculus VR and the HTC Vive have enabled virtual reality (VR) to emerge from the realm of cyberpunk science fiction to become a business opportunity. While VR is often considered through the lens of entertainment and gaming, forward-thinkers in the education sector are focusing on the technology's potential to transform and scale their operations.

The European Union will begin enforcement of its General Data Protection Regulation (GDPR) on May 25. Is your organization ready?

"The thing about GDPR is you never know when a breach is going to take place," says Steve Durbin, managing director of the Information Security Forum (ISF), a global, independent information security body that focuses on cyber security and information risk management. "One of the biggest challenges that I think comes with the GDPR is how do you enable an ongoing program within the enterprise? It's not a tick-box exercise. There is a fundamental change required in an enterprise in order to comply on an ongoing basis with the GDPR."

Together with social, mobile and cloud, analytics and associated data technologies have emerged as core business disruptors in the digital age. As companies began the shift from being data-generating to data-powered organizations in 2017, data and analytics became the center of gravity for many enterprises. In 2018, these technologies need to start delivering value. Here are the approaches, roles and concerns that will drive data analytics strategies in the year ahead.

Data lakes will need to demonstrate business value or die

Data has been accumulating in the enterprise at a torrid pace for years. The internet of things (IoT) will only accelerate the creation of data as data sources move from web to mobile to machines.

What do employees in your organization understand about security, data privacy, and compliance? According to a recent report from Bothell, Wash.-based MediaPro, perhaps not as much as they should. With data privacy fast becoming a hot-button issue, and the European Union's General Data Protection Regulation (GDPR) right around the corner, what your employees don’t know about handling data at your company could burn you.

The news isn’t all bad. In general, U.S.-based employees are proficient at identifying sensitive and private documents, and understand whether such data should be destroyed or securely stored. But they struggle with privacy regulations (particularly the GDPR and the EU-U.S. Privacy Shield), as well as handling sensitive data in their personal and professional lives.

2017 saw an explosion of machine learning in production use, with even deep learning and artificial intelligence (AI) being leveraged for practical applications.

"Basic analytics are out; machine learning (and beyond) are in," says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017.

Sanford says practical applications of machine learning, deep learning, and AI are "everywhere and out in the open these days," pointing to the "super billboards" in London's Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements.

The internet of things (IoT) and industrial internet of things (IIoT) will breakout in 2018, with organizations ramping up deployments and incorporating IoT technologies into their products, processes and workflows. Research firm Gartner predicts there will be nearly 20 billion devices connected to the IoT by 2020, and IoT product and service suppliers will generate more than $300 billion in revenue.

We spoke with a number of IT leaders and industry experts about what to expect from IoT deployments in the coming year. Following are six IoT trends to watch in 2018.

For nine years, Verizon has released its annual Payment Security Report about the state of Payment Card Industry Data Security Standard (PCI DSS) compliance. For nine years, the pattern has remained the same: Many companies don't comply with the standard, and many companies that do comply fall out of compliance not long after their audit. IT organizations don't struggle with PCI DSS compliance due to a lack of knowledge or technology; the problem is proficiency.

"Proficiency is the main theme," says Ciske van Oosten, lead author of the report since 2013 and senior manager of global intelligence for security assurance consulting at Verizon Enterprise Solutions. "With 10 years of data breach investigation reports, you start to recognize patterns."

What is DataOps?

DataOps (data operations) is an emerging discipline that brings together DevOps teams with data engineer and data scientist roles to provide the tools, processes and organizational structures to support the data-focused enterprise.

"You've got the modern trend for development of DevOps, but more and more people are injecting some sort of data science capability into development, into systems, so you need someone on the DevOps team who has a data frame of mind," says Ted Dunning, chief applications architect at MapR Technologies and co-author of Machine Learning Logistics: Model Management in the Real World.

If you thought 2017 was a dire year for data breaches, wait until 2018. The Information Security Forum (ISF), a global, independent information security body that focuses on cyber security and information risk management, forecasts an increase in the number and impact of data breaches, thanks in large part to five key global security threats that organizations will face in 2018.

"The scope and pace of information security threats is jeopardizing the veracity and reputation of today's most reliable organizations," says Steve Durbin, managing director of the ISF. "In 2018, we will see increased sophistication in the threat landscape with threats being personalized to their target's weak spots or metamorphosing to take account of defenses that have already been put in place. These days, the stakes are higher than ever before."

To make the most of your corporate data, your analysts should have universal access to data that can be understood by their tool of choice. But the siloed nature of data repositories, coupled with semantic data layers tailored to specific BI tools, have long scuttled that goal. Enter the universal semantic data layer, which, when applied to a data lake, can give your BI strategy a universal boost.

What is a universal semantic data layer?

A universal semantic data layer is a single business representation of all corporate data. It aims to help end users access all corporate data using common business terms via the business intelligence (BI) and analytics tools of their choice.

Data and big data analytics are fast becoming the lifeblood of any successful business. Getting the technology right can be challenging, but building the right team with the right skills to undertake big data initiatives can be even harder. Not surprisingly, that challenge is reflected in the rising demand for big data skills and certifications.

If you're looking for a way to get an edge, big data certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. The number of big data certs is expanding rapidly.